Effect of Socioeconomic Variables in Predicting Global Fire Ignition Occurrence

Abstract

Fires are a pervasive feature of the terrestrial biosphere and contribute large carbon emissions within the earth system. Humans are responsible for the majority of fire ignitions. Physical and empirical models are used to estimate the future effects of fires on vegetation dynamics and the Earth’s system. However, there is no consensus on how human-caused fire ignitions should be represented in such models. This study aimed to identify which globally available predictors of human activity explain global fire ignitions as observed by satellites. We applied a random forest machine learning framework to state-of-the-art global climate, vegetation, and land cover datasets to establish a baseline against which influences of socioeconomic data (cropland fraction, gross domestic product (GDP), road density, livestock density, grazed lands) on fire ignition occurrence were evaluated. Our results showed that a baseline random forest without human predictors captured the spatial patterns of fire ignitions globally, with hotspots over Sub-Saharan Africa and South East Asia. Adding single human predictors to the baseline model revealed that human variables vary in their effects on fire ignitions and that of the variables considered GDP is the most vital driver of fire ignitions. A combined model with all human predictors showed that the human variables improve the ignition predictions in most regions of the world, with some regions exhibiting worse predictions than the baseline model. We concluded that an ensemble of human predictors can add value to physical and empirical models. There are complex relationships between the variables, as evidenced by the improvement in bias in the combined model compared to the individual models. Furthermore, the variables tested have complex relationships that random forests may struggle to disentangle. Further work is required to detangle the complex regional relationships between these variables. These variables, e.g., population density, are well documented to have substantial effects on fire at local and regional scales; we determined that these variables may provide more insight at more continental scales.

Temporal–Spatial Characteristics and Influencing Factors of Forest Fires in the Tropic of Cancer (Yunnan Section)

“…The occurrence of forest fires within a certain range is not completely random but 21shows a certain spatiotemporal distribution pattern [12]. So, current studies of the spatiotemporal patterns of forest fire occurrence primarily include the frequency of forest fire occurrence on multiple spatial scales, such as global [13], national [14], provincial [15], and municipal [16], and the frequency of forest fire occurrence on different temporal scales, such as annual [17], monthly [18], and daily [19]. (2) The study on the influencing factors of forest fires.…”

Identifying Influential Spatial Drivers of Forest Fires through Geographically and Temporally Weighted Regression Coupled with a Continuous Invasive Weed Optimization Algorithm

“…However, it is worth noting that humans can also ignite forest fires through actions such as arson, smoking and discarding cigarettes, sparks from power lines, the use of explosives or fire during hunting, picnic fires, shepherds' fires, and stubble burning [11]. Mukunga et al [12] utilized datasets encompassing global climate, vegetation, land cover, and socioeconomic factors such as cropland fraction, GDP, road density, livestock density, and grazed lands. Their evaluation of ignition occurrences, employing a random forest machine learning technique, led them to the conclusion that incorporating human factors enhances the accuracy of predicting fire occurrences in most regions of the world.…”